simple_model_for_mnist.py

import keras

def preprocess_images(images, normalization=255):
  """Normalizes the inputs"""
  dims = images.shape
  return images.reshape((dims[0], dims[1]*dims[2])).astype('float32') / normalization

def preprocess_labels(labels):
  """Preprocesses the labels into categorical"""
  return keras.utils.to_categorical(labels)
  
def get_model(N=512):
  """Returns a simple FC model with N nodes in the first hidden layer"""
  model = keras.models.Sequential()
  model.add(keras.layers.Dense(N, activation='relu', input_shape=(28*28,)))
  model.add(keras.layers.Dense(10, activation='tanh'))
  model.compile(optimizer='rmsprop', loss='mse', metrics=['accuracy'])
  return model